Hierarchical clustering for crime rate mapping in Indonesia

Abstract

The Sustainable Development Goals (SDGs) are a blueprint for improving the human life quality. Goal 16 (G16) is related to security, and it is in line with the Universal Declaration of Human Rights and the Preamble to the 1945 Constitution. To support the implementation of the G16 achievement, the Indonesian National Police (Polri) has made serious efforts to provide a sense of safety for the community and to minimize crime rates. One of the efforts that could be made is to map areas based on the level of crimes so that the Polri can determine the appropriate strategy/priority of action for mitigation. Therefore, this study aimed to cluster provinces in Indonesia based on the four G16 indicators of the SDGs related to security, namely the number of homicide cases, the victim proportion, the proportion of people who feel safe walking alone in the area where they live, and the proportion of victims of violence that reported to the police in the past year using five hierarchical clustering methods, namely: Single-Linkage, Average-Linkage, Complete-Linkage, Ward, and Division Analysis. Then, methods were validated and compared using six cluster validations to obtain the most compact method. The results showed that Ward's method outperformed the others and produced three clusters. Clusters 1, 2, and 3 contained 18, 5, and 11 provinces respectively.

Keywords

• crime
• hierarchical clustering method
• machine learning
• sustainable development goals